Regional Models for the Estimation of Streamflow Series in Ungauged Basins

Cutore, P.; Cristaudo, Gabriella; Campisano, Alberto; Modica, Carlo; Cancelliere, Antonino; Rossi, Giuseppe

doi:10.1007/s11269-006-9110-7

Cited by 32 publications

(24 citation statements)

References 19 publications

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“…For example: in Cutore et al [47], the same attributes H and L were considered together with the permeable soil percentage, which could be considered as a proxy of the variable CN; Viola et al [55] In this study, Sicily as a whole has been initially assumed to be a homogeneous hydro-climatic region. As will be discussed in the following, this kind of approach has successively highlighted the need to consider a more efficient subdivision of Sicily into smaller homogeneous regions.…”

Section: Data Sources and Regional Databasementioning

confidence: 99%

“…For example: in Cutore et al [47], the same attributes H and L were considered together with the permeable soil percentage, which could be considered as a proxy of the variable CN; Viola et al [55] considered A, MAP, H, CN, and the permeable soil percentage of each basin (not considered here); Cannarozzo et al [56] used the variables A, L, H, and MAP.…”

Section: Data Sources and Regional Databasementioning

confidence: 99%

“…In addition to the precipitation, another variable that plays an important role in runoff modeling is the potential evapotranspiration, which represents a control factor for water losses from the soil due to evaporation and transpiration. In practical applications, temperature is often used as a proxy for the potential evapotranspiration [38,47,58], since this is rarely directly measured. Finally, the water accumulated in the soil (both saturated and unsaturated) could influence the slow response of the basins, providing further contributions to runoff generation due to the mechanisms of subsurface and groundwater runoff.…”

Section: Model Description and Assumptionsmentioning

confidence: 99%

See 2 more Smart Citations

Generation of Natural Runoff Monthly Series at Ungauged Sites Using a Regional Regressive Model

Pumo

Viola

Noto

2016

Water

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Many hydrologic applications require reliable estimates of runoff in river basins to face the widespread lack of data, both in time and in space. A regional method for the reconstruction of monthly runoff series is here developed and applied to Sicily (Italy). A simple modeling structure is adopted, consisting of a regression-based rainfall-runoff model with four model parameters, calibrated through a two-step procedure. Monthly runoff estimates are based on precipitation, temperature, and exploiting the autocorrelation with runoff at the previous month. Model parameters are assessed by specific regional equations as a function of easily measurable physical and climate basin descriptors. The first calibration step is aimed at the identification of a set of parameters optimizing model performances at the level of single basin. Such "optimal" sets are used at the second step, part of a regional regression analysis, to establish the regional equations for model parameters assessment as a function of basin attributes. All the gauged watersheds across the region have been analyzed, selecting 53 basins for model calibration and using the other six basins exclusively for validation. Performances, quantitatively evaluated by different statistical indexes, demonstrate relevant model ability in reproducing the observed hydrological time-series at both the monthly and coarser time resolutions. The methodology, which is easily transferable to other arid and semi-arid areas, provides a reliable tool for filling/reconstructing runoff time series at any gauged or ungauged basin of a region.

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Section: Data Sources and Regional Databasementioning

confidence: 99%

“…For example: in Cutore et al [47], the same attributes H and L were considered together with the permeable soil percentage, which could be considered as a proxy of the variable CN; Viola et al [55] considered A, MAP, H, CN, and the permeable soil percentage of each basin (not considered here); Cannarozzo et al [56] used the variables A, L, H, and MAP.…”

Section: Data Sources and Regional Databasementioning

confidence: 99%

Section: Model Description and Assumptionsmentioning

confidence: 99%

See 1 more Smart Citation

Generation of Natural Runoff Monthly Series at Ungauged Sites Using a Regional Regressive Model

Pumo

Viola

Noto

2016

Water

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“…A common solution is to use parameter regionalization, whereby model parameters are calibrated for gauged watersheds, and the parameters are then related to watershed characteristics through regression (Cutore et al, 2007;Hundecha et al, 2008). Micovic and Quick (1999) explored the applicability of constant model parameters to different watersheds.…”

Section: H Prediction In Ungauged Basinsmentioning

confidence: 99%

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

2012

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This article examines the current practice of streamflow modelling, a field under development for over a century. A sample of the wide range of assessment and planning applications of streamflow models is presented. The diversity in the use of these models is mirrored in the diversity of model complexity, and modelling approaches ranging from empirical to physically based and from lumped to fully distributed are described with examples. Predictions derived from hydrological models are subject to many sources of error; these are discussed along with methods for error minimization or anticipation. Model error is generally quantified using an ensemble of forecasts meant to sample the range of predictive uncertainty. This ensemble can be used to generate reliable probabilistic forecasts of hydrological quantities if all sources of error are accounted for. To date, applications of ensemble methods in streamflow forecasting have typically focused on only one or two error sources. A challenge will be to develop ensemble streamflow forecasts that sample a wider range of predictive uncertainty.

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“…Applications are known for a number of hydrologic models such as the IHACRES model by Jakeman et al (1990;see Sefton and Howarth 1998;Kokkonen et al 2003), the HBV model by Bergström (1995;see Seibert 1999;Merz and Blöschl 2004;Parajka et al 2007;Engeland and Hisdal 2009), the GR4J model (see Oudin et al 2008) and TOPMODEL (see Ao et al 2006), and data driven models (e.g. Cutore et al 2007). Various studies report on the effectiveness of the classical approach, but in Merz and Blöschl (2004) and Oudin et al (2008) the approach is outperformed by the spatial proximity approach.…”

Section: Regionalisation Of Model Parametersmentioning

confidence: 99%

Catchment Variability and Parameter Estimation in Multi-Objective Regionalisation of a Rainfall–Runoff Model

Deckers

Booij

Rientjes

et al. 2010

Water Resour Manage

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This study attempts to examine if catchment variability favours regionalisation by principles of catchment similarity. Our work combines calibration of a simple conceptual model for multiple objectives and multi-regression analyses to establish a regional model between model sensitive parameters and physical catchment characteristics (PCCs). The objective is to test robustness of regionalisation by assessing if generalisation of a wide range of climatic, topographic and physiographic settings in a regional model favours simulation of stream flow at ungauged catchments. Constraints in this work are very stringent performance measures for selection of catchments to establish the regional model and the selection of only PCCs that are available through the database of the National River Flow Archive in the United Kingdom. As such some PCCs have been ignored that have proven to be effective in other studies. For this study 56 well-gauged catchments in England and Wales are used. For model calibration and runoff simulation of ungauged catchments the HBV model is applied. Optimum parameter sets are derived for 48 catchments through Monte Carlo Simulation using an 8-year simulation period. This study aims to adequately simulate all aspects of the hydrograph at the ungauged catchment and therefore four single objective functions are combined in a multi-objective function. After calibration, 17 catchments that are widely spread over England and Wales are selected to establish relationships for seven selected model parameters using 14 PCCs (area, mean elevation, hypsometric integral, catchment shape, standard average annual rainfall, five types of land use and four classes of hydrogeology). Single and multiple regression analysis are applied to identify these relationships. For six model parameters statistically significant relationships could be established three of which are plausible on the basis of hydrologic interpretation. The established relationships are validated at eight gauged catchments that are spread over the UK and cover a large range of values of catchment descriptors. These catchments are assumed ungauged and results revealed that, in general, model parameters determined by the established regional relationships do not perform better as compared to default parameter values. Similar results are obtained for additional validation runs using catchments that are not used in the regionalisation procedure. Since these parameters are based on model performance assessments in a wide range of catchment settings, this suggests that large variability in settings of PCCs does not favour regionalisation. Therefore, for selected catchments the applicability of regionalisation by principles of catchment similarity for HBV model parameters may be questioned.

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Regional Models for the Estimation of Streamflow Series in Ungauged Basins

Cited by 32 publications

References 19 publications

Generation of Natural Runoff Monthly Series at Ungauged Sites Using a Regional Regressive Model

Generation of Natural Runoff Monthly Series at Ungauged Sites Using a Regional Regressive Model

Streamflow Modelling: A Primer on Applications, Approaches and Challenges

Catchment Variability and Parameter Estimation in Multi-Objective Regionalisation of a Rainfall–Runoff Model

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